tf-gan.py

# Generative Adversarial Network using TensorFlow
# TODO: save checkpoints

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from tensorflow.examples.tutorials.mnist import input_data

# Xavier init is well explained here in Andy's blog:
# http://andyljones.tumblr.com/post/110998971763/an-explanation-of-xavier-initialization
#
# Another good explanation can be found in Prateek Joshi's blog:
# https://prateekvjoshi.com/2016/03/29/understanding-xavier-initialization-in-deep-neural-networks/
def xavier_init(size):
    input_dim = size[0]
    xavier_variance = 1. / tf.sqrt(input_dim/2.)
    return tf.random_normal(shape=size, stddev=xavier_variance)

def plot(samples):
    fig = plt.figure(figsize=(4, 4))
    gs = gridspec.GridSpec(4, 4)
    gs.update(wspace=0.05, hspace=0.05)

    for i, sample in enumerate(samples):
        ax = plt.subplot(gs[i])
        plt.axis('off')
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect('equal')
        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')

    return fig

# Generator Net
Z = tf.placeholder(tf.float32, shape=[None, 100], name='Z')

G_W1 = tf.Variable(xavier_init([100, 128]), name='G_W1')
G_b1 = tf.Variable(tf.zeros(shape=[128]), name='G_b1')

G_W2 = tf.Variable(xavier_init([128, 784]), name='G_W2')
G_b2 = tf.Variable(tf.zeros(shape=[784]), name='G_b2')

theta_G = [G_W1, G_W2, G_b1, G_b2]

# Discriminator Net
X = tf.placeholder(tf.float32, shape=[None, 784], name='X')

D_W1 = tf.Variable(xavier_init([784, 128]), name='D_W1')
D_b1 = tf.Variable(tf.zeros(shape=[128]), name='D_b1')

D_W2 = tf.Variable(xavier_init([128, 1]), name='D_W2')
D_b2 = tf.Variable(tf.zeros(shape=[1]), name='D_b2')

theta_D = [D_W1, D_W2, D_b1, D_b2]

def generator(z):
    G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)
    G_log_prob = tf.matmul(G_h1, G_W2) + G_b2
    G_prob = tf.nn.sigmoid(G_log_prob)

    return G_prob

def discriminator(x):
    D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)
    D_logit = tf.matmul(D_h1, D_W2) + D_b2
    D_prob = tf.nn.sigmoid(D_logit)

    return D_prob, D_logit

G_sample = generator(Z)

D_real, D_logit_real = discriminator(X)
D_fake, D_logit_fake = discriminator(G_sample)

# Loss functions from the paper
# D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))
# G_loss = -tf.reduce_mean(tf.log(D_fake))

# Alternative loss functions
D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))
D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))
D_loss = D_loss_real + D_loss_fake
G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))

# Update D(X)'s parameters
D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)

# Update G(Z)'s parameters
G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)

def sample_Z(m, n):
    return np.random.uniform(-1., 1., size=[m, n])

batch_size = 128
Z_dim = 100

sess = tf.Session()
sess.run(tf.global_variables_initializer())

mnist = input_data.read_data_sets('MNIST/', one_hot=True)

if not os.path.exists('output/'):
    os.makedirs('output/')

i = 0

for itr in range(1000000):
    if itr % 1000 == 0:
        samples = sess.run(G_sample, feed_dict={Z: sample_Z(16, Z_dim)})

        fig = plot(samples)
        plt.savefig('output/{}.png'.format(str(i).zfill(3)), bbox_inches='tight')
        i += 1
        plt.close(fig)

    X_mb, _ = mnist.train.next_batch(batch_size)

    _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_mb, Z: sample_Z(batch_size, Z_dim)})
    _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: sample_Z(batch_size, Z_dim)})

    if itr % 1000 == 0:
        print('Iter: {}'.format(itr))
        print('D loss: {:.4}'. format(D_loss_curr))
        print('G_loss: {:.4}'.format(G_loss_curr))
        print()